Method and apparatus for point cloud compression

ABSTRACT

Aspects of the disclosure provide methods and apparatuses for point cloud compression and decompression. In some examples, an apparatus for point cloud compression/decompression includes processing circuitry. In some embodiments, the processing circuitry decodes prediction information of a point cloud from a coded bitstream and reconstructs a geometry reconstructed cloud according to a geometry image of the point cloud that is decoded from the coded bitstream. Further, the processing circuitry applies a filter to at least a geometry sample inside a patch of the geometry reconstructed cloud in addition to boundary samples of the patch to generate a smoothed geometry reconstructed cloud, and reconstructs points of the point cloud based on the smoothed geometry reconstructed cloud.

INCORPORATION BY REFERENCE

This present application claims the benefit of priority to U.S.Provisional Application No. 62/812,964, “TECHNIQUES AND APPARATUS FORSELECTIVE GEOMETRY SMOOTHING INSIDE PATCHES FOR POINT CLOUD COMPRESSION”filed on Mar. 1, 2019, which is incorporated by reference herein in itsentirety.

TECHNICAL FIELD

The present disclosure describes embodiments generally related to pointcloud compression.

BACKGROUND

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent the work is described in thisbackground section, as well as aspects of the description that may nototherwise qualify as prior art at the time of filing, are neitherexpressly nor impliedly admitted as prior art against the presentdisclosure.

Various technologies are developed to capture and represent world, suchas objects in the world, environments in the world, and the like in3-dimensional (3D) space. 3D representations of the world can enablemore immersive forms of interaction and communication. Point clouds canbe used as 3D representation of the world. A point cloud is a set ofpoints in a 3D space, each with associated attributes, e.g. color,material properties, texture information, intensity attributes,reflectivity attributes, motion related attributes, modality attributes,and various other attributes. Such point clouds may include largeamounts of data and may be costly and time-consuming to store andtransmit.

SUMMARY

Aspects of the disclosure provide methods and apparatuses for pointcloud compression and decompression. In some examples, an apparatus forpoint cloud compression/decompression includes processing circuitry.

According to some aspects of the disclosure, an apparatus for pointcloud decompression includes processing circuitry. The processingcircuitry decodes prediction information of a point cloud from a codedbitstream and reconstructs a geometry reconstructed cloud according to ageometry image of the point cloud that is decoded from the codedbitstream. Further, the processing circuitry applies a filter to atleast a geometry sample inside a patch of the geometry reconstructedcloud in addition to boundary samples of the patch to generate asmoothed geometry reconstructed cloud, and reconstructs points of thepoint cloud based on the smoothed geometry reconstructed cloud.

In some embodiments, the processing circuitry selects an area inside thepatch with a level of high frequency components higher than a thresholdlevel. In some examples, the processing circuitry detects edges insidethe patch based on depth values of the geometry reconstructed cloud.

In some embodiments, the processing circuitry selects an area inside thepatch with a level of motion content higher than a threshold level. Insome examples, the processing circuitry selects points inside the patchbased on motion information of corresponding pixels in the geometryimage.

In some embodiments, the prediction information includes a flagindicative of applying a selective smoothing inside patches of the pointcloud. In some examples, the prediction information is indicative of aspecific algorithm to select points inside the patches. Further, theprediction information includes parameters for the specific algorithm.

According to some aspects of the disclosure, an apparatus for pointcloud compression includes processing circuitry. The processingcircuitry compresses a geometry image associated with a point cloud andreconstructs a geometry reconstructed cloud according to the compressedgeometry image of the point cloud. Then, the processing circuitryapplies a filter to at least a geometry sample inside a patch of thegeometry reconstructed cloud in addition to boundary samples of thepatch to generate a smoothed geometry reconstructed cloud, and generatesa texture image for the point cloud based on the smoothed geometryreconstructed cloud.

In some embodiments, the processing circuitry selects an area inside thepatch with a level of high frequency components higher than a thresholdlevel. For example, the processing circuitry detects edges inside thepatch based on depth values of the geometry reconstructed cloud.

In some embodiments, the processing circuitry selects an area inside thepatch with a level of motion content higher than a threshold level. Forexample, the processing circuitry selects points inside the patch basedon motion information of corresponding pixels in the geometry image.

In some embodiments, the processing circuitry includes, in a codedbitstream for the compressed point cloud, a flag indicative of applyinga selective smoothing inside patches of the point cloud. In someexamples, the processing circuitry includes in the coded bitstream ofthe compressed point cloud, an indicator indicative of a specificalgorithm to select points inside the patches to apply the selectivesmoothing, and parameters for the specific algorithm.

Aspects of the disclosure also provide a non-transitorycomputer-readable medium storing instructions which when executed by acomputer for point cloud compression/decompression cause the computer toperform the method for point cloud compression/decompression.

BRIEF DESCRIPTION OF THE DRAWINGS

Further features, the nature, and various advantages of the disclosedsubject matter will be more apparent from the following detaileddescription and the accompanying drawings in which:

FIG. 1 is a schematic illustration of a simplified block diagram of acommunication system (100) in accordance with an embodiment.

FIG. 2 is a schematic illustration of a simplified block diagram of astreaming system (200) in accordance with an embodiment.

FIG. 3 shows a block diagram of an encoder (300) for encoding pointcloud frames, according to some embodiments.

FIG. 4 shows a block diagram of a decoder for decoding a compressedbitstream corresponding to point cloud frames according to someembodiments.

FIG. 5 is a schematic illustration of a simplified block diagram of avideo decoder in accordance with an embodiment.

FIG. 6 is a schematic illustration of a simplified block diagram of avideo encoder in accordance with an embodiment.

FIG. 7 shows a geometry image and a texture image for a point cloudaccording to some embodiments of the disclosure.

FIG. 8 shows an example of syntax according to some embodiments of thedisclosure.

FIG. 9 shows a flow chart outlining a process example according to someembodiments of the disclosure.

FIG. 10 shows a flow chart outlining a process example according to someembodiments of the disclosure.

FIG. 11 is a schematic illustration of a computer system in accordancewith an embodiment.

DETAILED DESCRIPTION OF EMBODIMENTS

Aspects of the disclosure provide point cloud coding techniques,specifically using video-coding for point cloud compression (V-PCC). TheV-PCC can utilize generic video codecs for point cloud compression. Thepoint cloud coding techniques in the present disclosure can improve bothlossless and lossy compression generated by the V-PCC.

A point cloud is a set of points in a 3D space, each with associatedattributes, e.g. color, material properties, texture information,intensity attributes, reflectivity attributes, motion relatedattributes, modality attributes, and various other attributes. Pointclouds can be used to reconstruct an object or a scene as a compositionof such points. The points can be captured using multiple cameras anddepth sensors in various setups and may be made up of thousands up tobillions of points in order to realistically represent reconstructedscenes.

Compression technologies are needed to reduce the amount of datarequired to represent a point cloud. As such, technologies are neededfor lossy compression of point clouds for use in real-timecommunications and six Degrees of Freedom (6 DoF) virtual reality. Inaddition, technology is sought for lossless point cloud compression inthe context of dynamic mapping for autonomous driving and culturalheritage applications, and the like. Moving picture experts group (MPEG)starts working on a standard to address compression of geometry andattributes such as colors and reflectance, scalable/progressive coding,coding of sequences of point clouds captured over time, and randomaccess to subsets of the point cloud.

According to an aspect of the disclosure, the main philosophy behindV-PCC is to leverage existing video codecs to compress the geometry,occupancy, and texture of a dynamic point cloud as three separate videosequences. The extra metadata needed to interpret the three videosequences are compressed separately. A small portion of the overallbitstream is the metadata, which could be encoded/decoded efficientlyusing software implementation. The bulk of the information is handled bythe video codec.

FIG. 1 illustrates a simplified block diagram of a communication system(100) according to an embodiment of the present disclosure. Thecommunication system (100) includes a plurality of terminal devices thatcan communicate with each other, via, for example, a network (150). Forexample, the communication system (100) includes a pair of terminaldevices (110) and (120) interconnected via the network (150). In theFIG. 1 example, the first pair of terminal devices (110) and (120) thatperform unidirectional transmission of point cloud data. For example,the terminal device (110) may compress point cloud (e.g., pointsrepresenting a structure) that are captured by a sensor 105 connectedwith the terminal device (110). The compressed point cloud can betransmitted, for example in the form of a bitstream, to the otherterminal device (120) via the network (150). The terminal device (120)may receive the compressed point cloud from the network (150),decompress the bitstream to reconstruct the point cloud and suitablydisplay according to the reconstructed point cloud. Unidirectional datatransmission may be common in media serving applications and the like.

In the FIG. 1 example, the terminal devices (110) and (120) may beillustrated as servers, and personal computers, but the principles ofthe present disclosure may be not so limited. Embodiments of the presentdisclosure find application with laptop computers, tablet computers,smart phones, gaming terminals, media players and/or dedicatedthree-dimensional (3D) equipment. The network (150) represents anynumber of networks that transmit compressed point cloud between theterminal devices (110) and (120). The network (150) can include forexample wireline (wired) and/or wireless communication networks. Thenetwork (150) may exchange data in circuit-switched and/orpacket-switched channels. Representative networks includetelecommunications networks, local area networks, wide area networksand/or the Internet. For the purposes of the present discussion, thearchitecture and topology of the network (150) may be immaterial to theoperation of the present disclosure unless explained herein below.

FIG. 2 illustrates, as an example for an application for the disclosedsubject matter for point cloud. The disclosed subject matter can beequally applicable to other point cloud enabled applications, including,3D telepresence application, virtual reality application.

A streaming system 200 may include a capture subsystem (213). Thecapture subsystem (213) can include a point cloud source (201), forexample light detection and ranging (LIDAR) systems, 3D cameras, 3Dscanners, a graphics generation component that generates theuncompressed point cloud in software, and like that generates forexample point clouds (202) that are uncompressed. In an example, thepoint clouds (202) include points that are captured by the 3D cameras.The point clouds (202), depicted as a bold line to emphasize a high datavolume when compared to compressed point clouds (204) (a bitstream ofcompressed point clouds). The compressed point clouds (204) can begenerated by an electronic device (220) that includes an encoder (203)coupled to the point cloud source (201). The encoder (203) can includehardware, software, or a combination thereof to enable or implementaspects of the disclosed subject matter as described in more detailbelow. The compressed point clouds (204) (or bitstream of compressedpoint clouds (204)), depicted as a thin line to emphasize the lower datavolume when compared to the stream of point clouds (202), can be storedon a streaming server (205) for future use. One or more streaming clientsubsystems, such as client subsystems (206) and (208) in FIG. 2 canaccess the streaming server (205) to retrieve copies (207) and (209) ofthe compressed point cloud (204). A client subsystem (206) can include adecoder (210), for example, in an electronic device (230). The decoder(210) decodes the incoming copy (207) of the compressed point clouds andcreates an outgoing stream of reconstructed point clouds (211) that canbe rendered on a rendering device (212). In some streaming systems, thecompressed point clouds (204), (207), and (209) (e.g., bitstreams ofcompressed point clouds) can be compressed according to certainstandards. In some examples, video coding standards are used in thecompression of point clouds. Examples of those standards include, HighEfficiency Video Coding (HEVC), Versatile Video Coding (VVC), and thelike.

It is noted that the electronic devices (220) and (230) can includeother components (not shown). For example, the electronic device (220)can include a decoder (not shown) and the electronic device (230) caninclude an encoder (not shown) as well.

FIG. 3 shows a block diagram of a V-PCC encoder (300) for encoding pointcloud frames, according to some embodiments. In some embodiments, theV-PCC encoder (300) can be used in the communication system (100) andstreaming system (200). For example, the encoder (203) can be configuredand operate in a similar manner as the V-PCC encoder (300).

The V-PCC encoder (300) receives point cloud frames that areuncompressed inputs and generates bitstream corresponding to compressedpoint cloud frames. In some embodiments, the V-PCC encoder (300) mayreceive the point cloud frames from a point cloud source, such as thepoint cloud source (201) and the like.

In the FIG. 3 example, the V-PCC encoder (300) includes a patchgeneration module 306, a patch packing module 308, a geometry imagegeneration module 310, a texture image generation module 312, a patchinfo module 304, an occupancy map module 314, a smoothing module 336,image padding modules 316 and 318, a group dilation module 320, videocompression modules 322, 323 and 332, an auxiliary patch infocompression module 338, an entropy compression module 334 and amultiplexer 324 coupled together as shown in FIG. 3.

According to an aspect of the disclosure, the V-PCC encoder (300),converts 3D point cloud frames into an image-based representation alongwith some meta data (e.g., occupancy map and patch info) necessary toconvert the compressed point cloud back into a decompressed point cloud.In some examples, the V-PCC encoder (300) can convert 3D point cloudframes into geometry images, texture images and occupancy maps, and thenuse video coding techniques to encode the geometry images, textureimages and occupancy maps into a bitstream. Generally, a geometry imageis a 2D image with pixels filled with geometry values associated withpoints projected to the pixels, and a pixel filled with a geometry valuecan be referred to as a geometry sample. A texture image is a 2D imagewith pixels filled with texture values associated with points projectedto the pixels and a pixel filled with texture value can be referred toas a texture sample. An occupancy map is a 2D image with pixels filledwith values that indicate occupied or unoccupied by patches.

The patch generation module (306) segments a point cloud into a set ofpatches (e.g., a patch is defined as a contiguous subset of the surfacedescribed by the point cloud), which may be overlapping or not, suchthat each patch may be described by a depth field with respect to aplane in 2D space. In some embodiments, the patch generation module(306) aims at decomposing the point cloud into a minimum number ofpatches with smooth boundaries, while also minimizing the reconstructionerror.

The patch info module (304) can collect the patch information thatindicates sizes and shapes of the patches. In some examples, the patchinformation can be packed into an image frame and then encoded by theauxiliary patch info compression module 338 to generate the compressedauxiliary patch information.

The patch packing module 308 is configured to map the extracted patchesonto a 2 dimensional (2D) grid while minimize the unused space andguarantee that every M×M (e.g., 16×16) block of the grid is associatedwith a unique patch. Efficient patch packing can directly impact thecompression efficiency either by minimizing the unused space or ensuringtemporal consistency.

The geometry image generation module (310) can generate 2D geometryimages associated with geometry of the point cloud at given patchlocations. The texture image generation module (312) can generate 2Dtexture images associated with texture of the point cloud at given patchlocations. The geometry image generation module 310 and the textureimage generation module (312) exploit the 3D to 2D mapping computedduring the packing process to store the geometry and texture of thepoint cloud as images. In order to better handle the case of multiplepoints being projected to the same sample, each patch is projected ontotwo images, referred to as layers. In an example, geometry image isrepresented by a monochromatic frame of W×H in YUV420-8 bit format. Togenerate the texture image, the texture generation procedure exploitsthe reconstructed/smoothed geometry in order to compute the colors to beassociated with the re-sampled points (also referred to as colortransfer).

The occupancy map module 314 can generate an occupancy map thatdescribes padding information at each unit. For example, the occupancyimage includes of a binary map that indicates for each cell of the gridwhether the cell belongs to the empty space or to the point cloud. In anexample, the occupancy map uses binary information describing for eachpixel whether the pixel is padded or not. In another example, theoccupancy map uses binary information describing for each block ofpixels whether the block of pixels is padded or not.

The occupancy map generated by the occupancy map module 314 can becompressed using lossless coding or lossy coding. When lossless codingis used, the entropy compression module 334 is used to compress theoccupancy map; when lossy coding is used, the video compression module332 is used to compress the occupancy map.

It is noted that the patch packing module 308 may leave some emptyspaces between 2D patches packed in an image frame. The image paddingmodule 316 and 318 can fill the empty spaces (referred to as padding) inorder to generate an image frame that may be suited for 2D video andimage codecs. The image padding is also referred to as backgroundfilling which can fill the unused space by redundant information. Insome examples, a good background filling minimally increases the bitrate while does not introduce significant coding distortion around thepatch boundaries.

The video compression modules 322, 323 and 332 can encode the 2D images,such as the padded geometry images, padded texture images, and occupancymaps based on a suitable video coding standard, such as HEVC, VVC andthe like. In an example, the video compression modules 322, 323 and 332are individual components that operate separately. It is noted that thevideo compression modules 322, 323 and 332 can be implemented as asingle component in another example.

In some examples, the smoothing module 336 is configured to generate asmoothed image of the reconstructed geometry image. The smoothed imageinformation can be provided to the texture image generation 312. Then,the texture image generation 312 may adjust the generation of thetexture image based on the reconstructed geometry images. For example,when a patch shape (e.g. geometry) is slightly distorted during encodingand decoding, the distortion may be taken into account when generatingthe texture images to correct for the distortion in patch shape.

In some embodiments, the group dilation 320 is configured to pad pixelsaround the object boundaries with redundant low-frequency content inorder to improve coding gain as well as visual quality of reconstructedpoint cloud.

The multiplexer 324 can multiplex the compressed geometry image, thecompressed texture image, the compressed occupancy map, the compressedauxiliary patch information into a compressed bitstream.

FIG. 4 shows a block diagram of a V-PCC decoder (400) for decodingcompressed bitstream corresponding to point cloud frames, according tosome embodiments. In some embodiments, the V-PCC decoder (400) can beused in the communication system (100) and streaming system (200). Forexample, the decoder (210) can be configured and operate in a similarmanner as the V-PCC decoder (400). The V-PCC decoder (400) receives thecompressed bitstream, and generates reconstructed point cloud based onthe compressed bitstream.

In the FIG. 4 example, the V-PCC decoder (400) includes a de-multiplexer(432), video decompression modules (434) and (436), an occupancy mapdecompression module (438), an auxiliary patch-information decompressionmodule (442), a geometry reconstruction module (444), a smoothing module(446), a texture reconstruction module (448) and a color smoothingmodule (452) coupled together as shown in FIG. 4.

The de-multiplexer (432) can receive the compressed bitstream andseparate into compressed texture image, compressed geometry image,compressed occupancy map and compressed auxiliary patch information.

The video decompression modules (434) and (436) can decode thecompressed images according to suitable standard (e.g., HEVC, VVC, etc.)and output decompressed images. For example, the video decompressionmodule (434) decodes the compressed texture images and outputsdecompressed texture images; and the video decompression module (436)decodes the compressed geometry images and outputs the decompressedgeometry images.

The occupancy map decompression module (438) can decode the compressedoccupancy maps according to suitable standard (e.g., HEVC, VVC, etc.)and output decompressed occupancy maps.

The auxiliary patch-information decompression module (442) can decodethe compressed auxiliary patch information according to suitablestandard (e.g., HEVC, VVC, etc.) and output decompressed auxiliary patchinformation.

The geometry reconstruction module (444) can receive the decompressedgeometry images, and generate reconstructed point cloud geometry basedon the decompressed occupancy map and decompressed auxiliary patchinformation.

The smoothing module (446) can smooth incongruences at edges of patches.The smoothing procedure aims at alleviating potential discontinuitiesthat may arise at the patch boundaries due to compression artifacts. Insome embodiments, a smoothing filter may be applied to the pixelslocated on the patch boundaries to alleviate the distortions that may becaused by the compression/decompression.

The texture reconstruction module (448) can determine textureinformation for points in the point cloud based on the decompressedtexture images and the smoothing geometry.

The color smoothing module (452) can smooth incongruences of coloring.Non-neighboring patches in 3D space are often packed next to each otherin 2D videos. In some examples, pixel values from non-neighboringpatches might be mixed up by the block-based video codec. The goal ofcolor smoothing is to reduce the visible artifacts that appear at patchboundaries.

FIG. 5 shows a block diagram of a video decoder (510) according to anembodiment of the present disclosure. The video decoder (510) can beused in the V-PCC decoder (400). For example, the video decompressionmodules (434) and (436), the occupancy map decompression module (438)can be similarly configured as the video decoder (510).

The video decoder (510) may include a parser (520) to reconstructsymbols (521) from compressed images, such as the coded video sequence.Categories of those symbols include information used to manage operationof the video decoder (510). The parser (520) may parse/entropy-decodethe coded video sequence that is received. The coding of the coded videosequence can be in accordance with a video coding technology orstandard, and can follow various principles, including variable lengthcoding, Huffman coding, arithmetic coding with or without contextsensitivity, and so forth. The parser (520) may extract from the codedvideo sequence, a set of subgroup parameters for at least one of thesubgroups of pixels in the video decoder, based upon at least oneparameter corresponding to the group. Subgroups can include Groups ofPictures (GOPs), pictures, tiles, slices, macroblocks, Coding Units(CUs), blocks, Transform Units (TUs), Prediction Units (PUs) and soforth. The parser (520) may also extract from the coded video sequenceinformation such as transform coefficients, quantizer parameter values,motion vectors, and so forth.

The parser (520) may perform an entropy decoding/parsing operation onthe video sequence received from a buffer memory, so as to createsymbols (521).

Reconstruction of the symbols (521) can involve multiple different unitsdepending on the type of the coded video picture or parts thereof (suchas: inter and intra picture, inter and intra block), and other factors.Which units are involved, and how, can be controlled by the subgroupcontrol information that was parsed from the coded video sequence by theparser (520). The flow of such subgroup control information between theparser (520) and the multiple units below is not depicted for clarity.

Beyond the functional blocks already mentioned, the video decoder (510)can be conceptually subdivided into a number of functional units asdescribed below. In a practical implementation operating undercommercial constraints, many of these units interact closely with eachother and can, at least partly, be integrated into each other. However,for the purpose of describing the disclosed subject matter, theconceptual subdivision into the functional units below is appropriate.

A first unit is the scaler/inverse transform unit (551). Thescaler/inverse transform unit (551) receives a quantized transformcoefficient as well as control information, including which transform touse, block size, quantization factor, quantization scaling matrices,etc. as symbol(s) (521) from the parser (520). The scaler/inversetransform unit (551) can output blocks comprising sample values, thatcan be input into aggregator (555).

In some cases, the output samples of the scaler/inverse transform (551)can pertain to an intra coded block; that is: a block that is not usingpredictive information from previously reconstructed pictures, but canuse predictive information from previously reconstructed parts of thecurrent picture. Such predictive information can be provided by an intrapicture prediction unit (552). In some cases, the intra pictureprediction unit (552) generates a block of the same size and shape ofthe block under reconstruction, using surrounding already reconstructedinformation fetched from the current picture buffer (558). The currentpicture buffer (558) buffers, for example, partly reconstructed currentpicture and/or fully reconstructed current picture. The aggregator(555), in some cases, adds, on a per sample basis, the predictioninformation the intra prediction unit (552) has generated to the outputsample information as provided by the scaler/inverse transform unit(551).

In other cases, the output samples of the scaler/inverse transform unit(551) can pertain to an inter coded, and potentially motion compensatedblock. In such a case, a motion compensation prediction unit (553) canaccess reference picture memory (557) to fetch samples used forprediction. After motion compensating the fetched samples in accordancewith the symbols (521) pertaining to the block, these samples can beadded by the aggregator (555) to the output of the scaler/inversetransform unit (551) (in this case called the residual samples orresidual signal) so as to generate output sample information. Theaddresses within the reference picture memory (557) from where themotion compensation prediction unit (553) fetches prediction samples canbe controlled by motion vectors, available to the motion compensationprediction unit (553) in the form of symbols (521) that can have, forexample X, Y, and reference picture components. Motion compensation alsocan include interpolation of sample values as fetched from the referencepicture memory (557) when sub-sample exact motion vectors are in use,motion vector prediction mechanisms, and so forth.

The output samples of the aggregator (555) can be subject to variousloop filtering techniques in the loop filter unit (556). Videocompression technologies can include in-loop filter technologies thatare controlled by parameters included in the coded video sequence (alsoreferred to as coded video bitstream) and made available to the loopfilter unit (556) as symbols (521) from the parser (520), but can alsobe responsive to meta-information obtained during the decoding ofprevious (in decoding order) parts of the coded picture or coded videosequence, as well as responsive to previously reconstructed andloop-filtered sample values.

The output of the loop filter unit (556) can be a sample stream that canbe output to a render device as well as stored in the reference picturememory (557) for use in future inter-picture prediction.

Certain coded pictures, once fully reconstructed, can be used asreference pictures for future prediction. For example, once a codedpicture corresponding to a current picture is fully reconstructed andthe coded picture has been identified as a reference picture (by, forexample, the parser (520)), the current picture buffer (558) can becomea part of the reference picture memory (557), and a fresh currentpicture buffer can be reallocated before commencing the reconstructionof the following coded picture.

The video decoder (510) may perform decoding operations according to apredetermined video compression technology in a standard, such as ITU-TRec. H.265. The coded video sequence may conform to a syntax specifiedby the video compression technology or standard being used, in the sensethat the coded video sequence adheres to both the syntax of the videocompression technology or standard and the profiles as documented in thevideo compression technology or standard. Specifically, a profile canselect certain tools as the only tools available for use under thatprofile from all the tools available in the video compression technologyor standard. Also necessary for compliance can be that the complexity ofthe coded video sequence is within bounds as defined by the level of thevideo compression technology or standard. In some cases, levels restrictthe maximum picture size, maximum frame rate, maximum reconstructionsample rate (measured in, for example megasamples per second), maximumreference picture size, and so on. Limits set by levels can, in somecases, be further restricted through Hypothetical Reference Decoder(HRD) specifications and metadata for HRD buffer management signaled inthe coded video sequence.

FIG. 6 shows a block diagram of a video encoder (603) according to anembodiment of the present disclosure. The video encoder (603) can beused in the V-PCC encoder (300) the compresses point clouds. In anexample, the video compression module (322) and (323), and the videocompression module (332) are configured similarly to the encoder (603).

The video encoder (603) may receive images, such as padded geometryimages, padded texture images and the like, and generate compressedimages.

According to an embodiment, the video encoder (603) may code andcompress the pictures of the source video sequence (images) into a codedvideo sequence (compressed images) in real time or under any other timeconstraints as required by the application. Enforcing appropriate codingspeed is one function of a controller (650). In some embodiments, thecontroller (650) controls other functional units as described below andis functionally coupled to the other functional units. The coupling isnot depicted for clarity. Parameters set by the controller (650) caninclude rate control related parameters (picture skip, quantizer, lambdavalue of rate-distortion optimization techniques, . . . ), picture size,group of pictures (GOP) layout, maximum motion vector search range, andso forth. The controller (650) can be configured to have other suitablefunctions that pertain to the video encoder (603) optimized for acertain system design.

In some embodiments, the video encoder (603) is configured to operate ina coding loop. As an oversimplified description, in an example, thecoding loop can include a source coder (630) (e.g., responsible forcreating symbols, such as a symbol stream, based on an input picture tobe coded, and a reference picture(s)), and a (local) decoder (633)embedded in the video encoder (603). The decoder (633) reconstructs thesymbols to create the sample data in a similar manner as a (remote)decoder also would create (as any compression between symbols and codedvideo bitstream is lossless in the video compression technologiesconsidered in the disclosed subject matter). The reconstructed samplestream (sample data) is input to the reference picture memory (634). Asthe decoding of a symbol stream leads to bit-exact results independentof decoder location (local or remote), the content in the referencepicture memory (634) is also bit exact between the local encoder andremote encoder. In other words, the prediction part of an encoder “sees”as reference picture samples exactly the same sample values as a decoderwould “see” when using prediction during decoding. This fundamentalprinciple of reference picture synchronicity (and resulting drift, ifsynchronicity cannot be maintained, for example because of channelerrors) is used in some related arts as well.

The operation of the “local” decoder (633) can be the same as of a“remote” decoder, such as the video decoder (510), which has alreadybeen described in detail above in conjunction with FIG. 5. Brieflyreferring also to FIG. 5, however, as symbols are available andencoding/decoding of symbols to a coded video sequence by an entropycoder (645) and the parser (520) can be lossless, the entropy decodingparts of the video decoder (510), including and parser (520) may not befully implemented in the local decoder (633).

An observation that can be made at this point is that any decodertechnology except the parsing/entropy decoding that is present in adecoder also necessarily needs to be present, in substantially identicalfunctional form, in a corresponding encoder. For this reason, thedisclosed subject matter focuses on decoder operation. The descriptionof encoder technologies can be abbreviated as they are the inverse ofthe comprehensively described decoder technologies. Only in certainareas a more detail description is required and provided below.

During operation, in some examples, the source coder (630) may performmotion compensated predictive coding, which codes an input picturepredictively with reference to one or more previously-coded picture fromthe video sequence that were designated as “reference pictures”. In thismanner, the coding engine (632) codes differences between pixel blocksof an input picture and pixel blocks of reference picture(s) that may beselected as prediction reference(s) to the input picture.

The local video decoder (633) may decode coded video data of picturesthat may be designated as reference pictures, based on symbols createdby the source coder (630). Operations of the coding engine (632) mayadvantageously be lossy processes. When the coded video data may bedecoded at a video decoder (not shown in FIG. 6), the reconstructedvideo sequence typically may be a replica of the source video sequencewith some errors. The local video decoder (633) replicates decodingprocesses that may be performed by the video decoder on referencepictures and may cause reconstructed reference pictures to be stored inthe reference picture cache (634). In this manner, the video encoder(603) may store copies of reconstructed reference pictures locally thathave common content as the reconstructed reference pictures that will beobtained by a far-end video decoder (absent transmission errors).

The predictor (635) may perform prediction searches for the codingengine (632). That is, for a new picture to be coded, the predictor(635) may search the reference picture memory (634) for sample data (ascandidate reference pixel blocks) or certain metadata such as referencepicture motion vectors, block shapes, and so on, that may serve as anappropriate prediction reference for the new pictures. The predictor(635) may operate on a sample block-by-pixel block basis to findappropriate prediction references. In some cases, as determined bysearch results obtained by the predictor (635), an input picture mayhave prediction references drawn from multiple reference pictures storedin the reference picture memory (634).

The controller (650) may manage coding operations of the source coder(630), including, for example, setting of parameters and subgroupparameters used for encoding the video data.

Output of all aforementioned functional units may be subjected toentropy coding in the entropy coder (645). The entropy coder (645)translates the symbols as generated by the various functional units intoa coded video sequence, by lossless compressing the symbols according totechnologies such as Huffman coding, variable length coding, arithmeticcoding, and so forth.

The controller (650) may manage operation of the video encoder (603).During coding, the controller (650) may assign to each coded picture acertain coded picture type, which may affect the coding techniques thatmay be applied to the respective picture. For example, pictures oftenmay be assigned as one of the following picture types:

An Intra Picture (I picture) may be one that may be coded and decodedwithout using any other picture in the sequence as a source ofprediction. Some video codecs allow for different types of intrapictures, including, for example Independent Decoder Refresh (“IDR”)Pictures. A person skilled in the art is aware of those variants of Ipictures and their respective applications and features.

A predictive picture (P picture) may be one that may be coded anddecoded using intra prediction or inter prediction using at most onemotion vector and reference index to predict the sample values of eachblock.

A bi-directionally predictive picture (B Picture) may be one that may becoded and decoded using intra prediction or inter prediction using atmost two motion vectors and reference indices to predict the samplevalues of each block. Similarly, multiple-predictive pictures can usemore than two reference pictures and associated metadata for thereconstruction of a single block.

Source pictures commonly may be subdivided spatially into a plurality ofsample blocks (for example, blocks of 4×4, 8×8, 4×8, or 16×16 sampleseach) and coded on a block-by-block basis. Blocks may be codedpredictively with reference to other (already coded) blocks asdetermined by the coding assignment applied to the blocks' respectivepictures. For example, blocks of I pictures may be codednon-predictively or they may be coded predictively with reference toalready coded blocks of the same picture (spatial prediction or intraprediction). Pixel blocks of P pictures may be coded predictively, viaspatial prediction or via temporal prediction with reference to onepreviously coded reference picture. Blocks of B pictures may be codedpredictively, via spatial prediction or via temporal prediction withreference to one or two previously coded reference pictures.

The video encoder (603) may perform coding operations according to apredetermined video coding technology or standard, such as ITU-T Rec.H.265. In its operation, the video encoder (603) may perform variouscompression operations, including predictive coding operations thatexploit temporal and spatial redundancies in the input video sequence.The coded video data, therefore, may conform to a syntax specified bythe video coding technology or standard being used.

A video may be in the form of a plurality of source pictures (images) ina temporal sequence. Intra-picture prediction (often abbreviated tointra prediction) makes use of spatial correlation in a given picture,and inter-picture prediction makes uses of the (temporal or other)correlation between the pictures. In an example, a specific pictureunder encoding/decoding, which is referred to as a current picture, ispartitioned into blocks. When a block in the current picture is similarto a reference block in a previously coded and still buffered referencepicture in the video, the block in the current picture can be coded by avector that is referred to as a motion vector. The motion vector pointsto the reference block in the reference picture, and can have a thirddimension identifying the reference picture, in case multiple referencepictures are in use.

In some embodiments, a bi-prediction technique can be used in theinter-picture prediction. According to the bi-prediction technique, tworeference pictures, such as a first reference picture and a secondreference picture that are both prior in decoding order to the currentpicture in the video (but may be in the past and future, respectively,in display order) are used. A block in the current picture can be codedby a first motion vector that points to a first reference block in thefirst reference picture, and a second motion vector that points to asecond reference block in the second reference picture. The block can bepredicted by a combination of the first reference block and the secondreference block.

Further, a merge mode technique can be used in the inter-pictureprediction to improve coding efficiency.

According to some embodiments of the disclosure, predictions, such asinter-picture predictions and intra-picture predictions are performed inthe unit of blocks. For example, according to the HEVC standard, apicture in a sequence of video pictures is partitioned into coding treeunits (CTU) for compression, the CTUs in a picture have the same size,such as 64×64 pixels, 32×32 pixels, or 16×16 pixels. In general, a CTUincludes three coding tree blocks (CTBs), which are one luma CTB and twochroma CTBs. Each CTU can be recursively quadtree split into one ormultiple coding units (CUs). For example, a CTU of 64×64 pixels can besplit into one CU of 64×64 pixels, or 4 CUs of 32×32 pixels, or 16 CUsof 16×16 pixels. In an example, each CU is analyzed to determine aprediction type for the CU, such as an inter prediction type or an intraprediction type. The CU is split into one or more prediction units (PUs)depending on the temporal and/or spatial predictability. Generally, eachPU includes a luma prediction block (PB), and two chroma PBs. In anembodiment, a prediction operation in coding (encoding/decoding) isperformed in the unit of a prediction block. Using a luma predictionblock as an example of a prediction block, the prediction block includesa matrix of values (e.g., luma values) for pixels, such as 8×8 pixels,16×16 pixels, 8×16 pixels, 16×8 pixels, and the like.

According to some aspects of the disclosure, geometry smoothing can beperformed by both the encoder side (for point cloud compression) and thedecoder side (for point cloud reconstruction). In an example, at theencoder side, after compression of geometry video, the geometry portionof the point cloud is reconstructed using the compressed geometry videoand the corresponding occupancy map, and the reconstructed point cloud(geometry portion) is referred to as geometry reconstructed cloud. Thegeometry reconstructed cloud is used for generating the texture images.For example, the texture image generation 312 can determine colors to beassociated with the re-sampled points in the geometry reconstructedcloud (also referred to as color transfer) and generate texture imagesaccordingly.

In some examples, geometry smoothing is applied on the geometryreconstructed cloud before color transfer. For example, the smoothingmodule 336 can apply smoothing (e.g., smooth filter) on the geometryreconstructed cloud that is generated based on the reconstructedgeometry images. In some embodiments of the present disclosure, thesmoothing module 336 is configured to not only recover the geometrydistortions at patch boundaries, but also recover the geometrydistortions inside the patches.

At the decoder side, using the V-PCC decoder 400 in FIG. 4 as anexample, the smoothing module 446, can apply smoothing to the geometryreconstructed cloud, and generate smoothed geometry reconstructed cloud.Then, the texture reconstruction module 448 can determine textureinformation for points in the point cloud based on the decompressedtexture images and the smoothed geometry reconstructed cloud.

According to some aspects of the disclosure, distortions can happen dueto quantization errors during geometry compression and/or conversion ofa high-resolution occupancy map to a lower-resolution map. Thequantization errors can affect the patch boundaries, and can affect thereconstructed depth values (which is the geometry information of points)inside the patches which could lead to unsmooth reconstructed surfaces.The present disclosure provides techniques to smooth reconstructed depthvalues inside the patches.

The proposed methods may be used separately or combined in any order.Further, each of the methods (or embodiments), encoder, and decoder maybe implemented by processing circuitry (e.g., one or more processors orone or more integrated circuits). In one example, the one or moreprocessors execute a program that is stored in a non-transitorycomputer-readable medium.

FIG. 7 shows a geometry image 710 and a texture image 750 for a pointcloud. The point cloud is decomposed into a plurality of patches. Insome related examples, smoothing is applied only to the patchboundaries, such as the boundaries shown by 711 in FIG. 7. In thepresent disclosure, smoothing can be applied to certain locations insidethe patches, such as shown by 721. The locations can be selected basedon certain criteria. The smoothing is applied inside the patches in aselective manner so that minimal additional computational complexity isincurred. In some embodiments, candidate points whose reconstructeddepth values differ the most compared to uncompressed depth values canbe determined and added to a list. The list can also include boundarypoints. Then, smoothing can be applied to the points in the list, suchas by the smoothing module 336, the smoothing module 446, and the like.

In some embodiments, a set of candidate points inside the patches to besmoothed by the smoothing filter can be derived based on thereconstructed depth values. In some embodiments, a suitable algorithm isused on both the encoder side and the decoder side to select, forexample based on estimation, candidate points whose reconstructed depthvalues differ the most from the original uncompressed values, which arenot available at the decoder side. In some examples, candidate pointsare selected as the ones whose reconstructed depth values are believedto have relatively large quantization errors. In an example, areas withhigh frequency components (high spatial frequency components) in depthmap (e.g., reconstructed geometry image) can be selected. For examples,when a ratio of the strength of high spatial frequency components to thestrength of low spatial frequency components in an area is higher than athreshold, the area is high frequency area with a relatively high levelof high spatial frequency components, and the area can be selected forapplying a smoothing filter. In another example, areas with high motioncontent in depth map (e.g., reconstructed geometry image) can beselected. For example, the area can be selected based on motion vectorinformation that is generally used in video codec.

In some embodiments, edge detection can be applied to the depth map(e.g., reconstructed geometry image) to determine points correspondingto edges inside the patches and smoothing can be applied to the pointscorresponding to the edges located inside the patches. Generally, edgeareas have relatively high spatial frequency components.

In some embodiments, candidate points can be derived based on theinformation implicitly provided by the video compression tool (e.g.HEVC) used by V-PCC to compress/decompress the depth map. In an example,pixels with large motion vectors can be selected and pointscorresponding to the pixels with large motion vectors can be selected ascandidate points and added to the list to be applied with smoothing. Inanother example, pixels whose response to sample adaptive offset (SAO)is relatively large, can be selected and points corresponding to thepixels with large response to SAO can be selected as candidate pointsand added to the list to be applied with smoothing.

According to some aspects of the disclosure, the encoder side and thedecoder side use the same algorithm to determine the points (or areas)inside the patches to apply the smoothing. In some embodiments, flagsand parameters can be included in the coded bitstream, thus the decoderside can determine the algorithm and parameters that are used by theencoder for selecting the points inside the patches to apply thesmoothing, and then the decoder side can use the same algorithm andparameters to select points inside patches to apply the smoothing.

FIG. 8 shows an example of syntax according to some embodiments of thedisclosure. In the FIG. 8 example,selective_smoothing_inside_patches_present_flag is used to indicatewhether selective smoothing inside patches is used or not. In anexample, when selective_smoothing_inside_patches_present_flag is true,an algorithm can be indicated, for example by a parameter denoted byalgorithm_to_find_candidates_inside_patches.

Further, in an example, when the algorithm is an edge detectionalgorithm, parameters used in the edge detection algorithm can beindicated, such as the size of the kernel of the edge detectionalgorithm that is denoted by kernel_size, the values in the kernel withrespect to the raster scan order that are denoted by kernel[i], i=0 . .. kernel_size×kernel_size, and the like.

It is noted that in FIG. 8, XYZ denotes other suitable algorithm toselect candidate points inside patches to apply smoothing, andXYZ_parameters denote the values of the parameters to be used foralgorithm XYZ.

FIG. 9 shows a flow chart outlining a process (900) according to anembodiment of the disclosure. The process (900) can be used during anencoding process for encoding point clouds. In various embodiments, theprocess (900) is executed by processing circuitry, such as theprocessing circuitry in the terminal devices (110), the processingcircuitry that performs functions of the encoder (203), the processingcircuitry that performs functions of the V-PCC encoder (300), and thelike. In some embodiments, the process (900) is implemented in softwareinstructions, thus when the processing circuitry executes the softwareinstructions, the processing circuitry performs the process (900). Theprocess starts at (S901) and proceeds to (S910).

At (S910), a geometry image associated with a point cloud is compressed.In an example, the patch generation module 306 can generate patches fora point cloud. Further, the geometry image generation module 310 storesthe geometry information, such as e.g. depth values of points, asgeometry images. The video compression module 322 can compress geometryimages associated with the point cloud.

At (S920), a geometry reconstructed cloud according to the compressedgeometry image is generated. In an example, the video compression module322 can generate the reconstructed geometry images according to thecompressed geometry images. The reconstructed geometry images can beused to form the geometry reconstructed cloud.

At (S930), a smooth filter is applied to at least a geometry sampleinside a patch of the geometry reconstructed cloud in addition toboundary samples of the patch. In some examples, the smoothing module336 can apply a smooth filter on boundary points of a patch. Inaddition, the smoothing module 336 selectively applies the smooth filterto some points that are inside the patch. In some embodiments, pointswhose reconstructed depth values may differ the most from the originaluncompressed values can be selected based on estimation. For example,points in the area that has high level of high spatial frequencycomponents can be selected. In another example, points with high motioncontent in the depth map (e.g., determined based on motion vectorinformation provided by the video compression module 322) can beselected.

At (S940), texture image is generated based on the smoothed geometryreconstructed cloud. In an example, the texture image generation module312 can determine colors to be associated with the re-sampled points inthe smoothed geometry reconstructed cloud (also referred to as colortransfer) and generate texture images accordingly.

At (S950), the texture image is compressed. In an example, videocompression module 323 can generate the compressed texture image. Then,the compressed geometry image, the compressed texture image, and othersuitable information can be multiplexed to form a coded bitstream. Insome examples, flags and parameters associated with the selectivegeometry smoothing inside patches can be included in the codedbitstream. Then, the process proceeds to (S999) and terminates.

FIG. 10 shows a flow chart outlining a process (1000) according to anembodiment of the disclosure. The process (1000) can be used during adecoding process for reconstructing point clouds. In variousembodiments, the process (1000) is executed by processing circuitry,such as the processing circuitry in the terminal devices (120), theprocessing circuitry that performs functions of the decoder (210), theprocessing circuitry that performs functions of the V-PCC decoder (400),and the like. In some embodiments, the process (1000) is implemented insoftware instructions, thus when the processing circuitry executes thesoftware instructions, the processing circuitry performs the process(1000). The process starts at (S1001) and proceeds to (S1010).

At (S1010), prediction information of an image is decoded from a codedbitstream corresponding to a point cloud. In some examples, theprediction information includes flags and parameters associated withselective geometry smoothing inside patches.

At (S1020), a geometry reconstructed cloud is generated according to ageometry image that is decoded from the coded bitstream. In an example,the video decompression module 436 can decode geometry information, andgenerate decompressed geometry image(s). The geometry reconstructionmodule 444 can generate geometry reconstructed cloud based on thedecompressed geometry image(s).

At (S1030), a smooth filter is applied to at least a geometry sampleinside a patch of the geometry reconstructed cloud in addition toboundary samples of the patch. In some examples, the smoothing module446 can apply a smooth filter on the geometry samples for the boundarypoints of a patch. In addition, the smoothing module 446 selectivelyapplies the smooth filter to geometry samples for some points that areinside the patch. In some embodiments, points whose reconstructed depthvalues may differ the most from the original uncompressed values can beselected based on estimation. For example, points in the area that hashigh level of high spatial frequency components can be selected. Inanother example, points with high motion content in the depth map (e.g.,determined based on motion vector information provided by the videodecompression module 436) can be selected.

At (S1040), the point cloud is reconstructed based on the smoothedgeometry reconstructed cloud. For example, the texture reconstructionmodule (448) can determine texture information for points in the pointcloud based on the decompressed texture images and the smoothed geometryreconstructed cloud. Then, the color smoothing module (452) can smoothincongruences of coloring. Then, the process proceeds to (S1099) andterminates.

The techniques described above, can be implemented as computer softwareusing computer-readable instructions and physically stored in one ormore computer-readable media. For example, FIG. 11 shows a computersystem (1100) suitable for implementing certain embodiments of thedisclosed subject matter.

The computer software can be coded using any suitable machine code orcomputer language, that may be subject to assembly, compilation,linking, or like mechanisms to create code comprising instructions thatcan be executed directly, or through interpretation, micro-codeexecution, and the like, by one or more computer central processingunits (CPUs), Graphics Processing Units (GPUs), and the like.

The instructions can be executed on various types of computers orcomponents thereof, including, for example, personal computers, tabletcomputers, servers, smartphones, gaming devices, internet of thingsdevices, and the like.

The components shown in FIG. 11 for computer system (1100) are exemplaryin nature and are not intended to suggest any limitation as to the scopeof use or functionality of the computer software implementingembodiments of the present disclosure. Neither should the configurationof components be interpreted as having any dependency or requirementrelating to any one or combination of components illustrated in theexemplary embodiment of a computer system (1100).

Computer system (1100) may include certain human interface inputdevices. Such a human interface input device may be responsive to inputby one or more human users through, for example, tactile input (such as:keystrokes, swipes, data glove movements), audio input (such as: voice,clapping), visual input (such as: gestures), olfactory input (notdepicted). The human interface devices can also be used to capturecertain media not necessarily directly related to conscious input by ahuman, such as audio (such as: speech, music, ambient sound), images(such as: scanned images, photographic images obtain from a still imagecamera), video (such as two-dimensional video, three-dimensional videoincluding stereoscopic video).

Input human interface devices may include one or more of (only one ofeach depicted): keyboard (1101), mouse (1102), trackpad (1103), touchscreen (1110), data-glove (not shown), joystick (1105), microphone(1106), scanner (1107), camera (1108).

Computer system (1100) may also include certain human interface outputdevices. Such human interface output devices may be stimulating thesenses of one or more human users through, for example, tactile output,sound, light, and smell/taste. Such human interface output devices mayinclude tactile output devices (for example tactile feedback by thetouch-screen (1110), data-glove (not shown), or joystick (1105), butthere can also be tactile feedback devices that do not serve as inputdevices), audio output devices (such as: speakers (1109), headphones(not depicted)), visual output devices (such as screens (1110) toinclude CRT screens, LCD screens, plasma screens, OLED screens, eachwith or without touch-screen input capability, each with or withouttactile feedback capability—some of which may be capable to output twodimensional visual output or more than three dimensional output throughmeans such as stereographic output; virtual-reality glasses (notdepicted), holographic displays and smoke tanks (not depicted)), andprinters (not depicted).

Computer system (1100) can also include human accessible storage devicesand their associated media such as optical media including CD/DVD ROM/RW(1120) with CD/DVD or the like media (1121), thumb-drive (1122),removable hard drive or solid state drive (1123), legacy magnetic mediasuch as tape and floppy disc (not depicted), specialized ROM/ASIC/PLDbased devices such as security dongles (not depicted), and the like.

Those skilled in the art should also understand that term “computerreadable media” as used in connection with the presently disclosedsubject matter does not encompass transmission media, carrier waves, orother transitory signals.

Computer system (1100) can also include an interface to one or morecommunication networks. Networks can for example be wireless, wireline,optical. Networks can further be local, wide-area, metropolitan,vehicular and industrial, real-time, delay-tolerant, and so on. Examplesof networks include local area networks such as Ethernet, wireless LANs,cellular networks to include GSM, 3G, 4G, 5G, LTE and the like, TVwireline or wireless wide area digital networks to include cable TV,satellite TV, and terrestrial broadcast TV, vehicular and industrial toinclude CANBus, and so forth. Certain networks commonly require externalnetwork interface adapters that attached to certain general purpose dataports or peripheral buses (1149) (such as, for example USB ports of thecomputer system (1100)); others are commonly integrated into the core ofthe computer system (1100) by attachment to a system bus as describedbelow (for example Ethernet interface into a PC computer system orcellular network interface into a smartphone computer system). Using anyof these networks, computer system (1100) can communicate with otherentities. Such communication can be uni-directional, receive only (forexample, broadcast TV), uni-directional send-only (for example CANbus tocertain CANbus devices), or bi-directional, for example to othercomputer systems using local or wide area digital networks. Certainprotocols and protocol stacks can be used on each of those networks andnetwork interfaces as described above.

Aforementioned human interface devices, human-accessible storagedevices, and network interfaces can be attached to a core (1140) of thecomputer system (1100).

The core (1140) can include one or more Central Processing Units (CPU)(1141), Graphics Processing Units (GPU) (1142), specialized programmableprocessing units in the form of Field Programmable Gate Areas (FPGA)(1143), hardware accelerators for certain tasks (1144), and so forth.These devices, along with Read-only memory (ROM) (1145), Random-accessmemory (1146), internal mass storage such as internal non-useraccessible hard drives, SSDs, and the like (1147), may be connectedthrough a system bus (1148). In some computer systems, the system bus(1148) can be accessible in the form of one or more physical plugs toenable extensions by additional CPUs, GPU, and the like. The peripheraldevices can be attached either directly to the core's system bus (1148),or through a peripheral bus (1149). Architectures for a peripheral businclude PCI, USB, and the like.

CPUs (1141), GPUs (1142), FPGAs (1143), and accelerators (1144) canexecute certain instructions that, in combination, can make up theaforementioned computer code. That computer code can be stored in ROM(1145) or RAM (1146). Transitional data can be also be stored in RAM(1146), whereas permanent data can be stored for example, in theinternal mass storage (1147). Fast storage and retrieve to any of thememory devices can be enabled through the use of cache memory, that canbe closely associated with one or more CPU (1141), GPU (1142), massstorage (1147), ROM (1145), RAM (1146), and the like.

The computer readable media can have computer code thereon forperforming various computer-implemented operations. The media andcomputer code can be those specially designed and constructed for thepurposes of the present disclosure, or they can be of the kind wellknown and available to those having skill in the computer software arts.

As an example and not by way of limitation, the computer system havingarchitecture (1100), and specifically the core (1140) can providefunctionality as a result of processor(s) (including CPUs, GPUs, FPGA,accelerators, and the like) executing software embodied in one or moretangible, computer-readable media. Such computer-readable media can bemedia associated with user-accessible mass storage as introduced above,as well as certain storage of the core (1140) that are of non-transitorynature, such as core-internal mass storage (1147) or ROM (1145). Thesoftware implementing various embodiments of the present disclosure canbe stored in such devices and executed by core (1140). Acomputer-readable medium can include one or more memory devices orchips, according to particular needs. The software can cause the core(1140) and specifically the processors therein (including CPU, GPU,FPGA, and the like) to execute particular processes or particular partsof particular processes described herein, including defining datastructures stored in RAM (1146) and modifying such data structuresaccording to the processes defined by the software. In addition or as analternative, the computer system can provide functionality as a resultof logic hardwired or otherwise embodied in a circuit (for example:accelerator (1144)), which can operate in place of or together withsoftware to execute particular processes or particular parts ofparticular processes described herein. Reference to software canencompass logic, and vice versa, where appropriate. Reference to acomputer-readable media can encompass a circuit (such as an integratedcircuit (IC)) storing software for execution, a circuit embodying logicfor execution, or both, where appropriate. The present disclosureencompasses any suitable combination of hardware and software.

Appendix A: Acronyms

JEM: joint exploration modelVVC: versatile video codingBMS: benchmark set

MV: Motion Vector HEVC: High Efficiency Video Coding SEI: SupplementaryEnhancement Information VUI: Video Usability Information GOPs: Groups ofPictures TUs: Transform Units, PUs: Prediction Units CTUs: Coding TreeUnits CTBs: Coding Tree Blocks PBs: Prediction Blocks HRD: HypotheticalReference Decoder SNR: Signal Noise Ratio CPUs: Central Processing UnitsGPUs: Graphics Processing Units CRT: Cathode Ray Tube LCD:Liquid-Crystal Display OLED: Organic Light-Emitting Diode CD: CompactDisc DVD: Digital Video Disc ROM: Read-Only Memory RAM: Random AccessMemory ASIC: Application-Specific Integrated Circuit PLD: ProgrammableLogic Device LAN: Local Area Network

GSM: Global System for Mobile communications

LTE: Long-Term Evolution CANBus: Controller Area Network Bus USB:Universal Serial Bus PCI: Peripheral Component Interconnect FPGA: FieldProgrammable Gate Areas

SSD: solid-state drive

IC: Integrated Circuit CU: Coding Unit

While this disclosure has described several exemplary embodiments, thereare alterations, permutations, and various substitute equivalents, whichfall within the scope of the disclosure. It will thus be appreciatedthat those skilled in the art will be able to devise numerous systemsand methods which, although not explicitly shown or described herein,embody the principles of the disclosure and are thus within the spiritand scope thereof.

What is claimed is:
 1. A method for point cloud decompression,comprising: decoding, by a processor, prediction information of a pointcloud from a coded bitstream; reconstructing, by the processor, ageometry reconstructed cloud according to a geometry image of the pointcloud that is decoded from the coded bitstream; applying, by theprocessor, a filter to at least a geometry sample inside a patch of thegeometry reconstructed cloud in addition to boundary samples of thepatch to generate a smoothed geometry reconstructed cloud; andreconstructing, by the processor, points of the point cloud based on thesmoothed geometry reconstructed cloud.
 2. The method of claim 1, furthercomprising: selecting, by the processor, an area inside the patch with alevel of high frequency components higher than a threshold level.
 3. Themethod of claim 1, further comprising: selecting, by the processor, anarea inside the patch with a level of motion content higher than athreshold level.
 4. The method of claim 2, further comprising:detecting, by the processor, edges inside the patch based on depthvalues of the geometry reconstructed cloud.
 5. The method of claim 3,further comprising: selecting, by the processor, points inside the patchbased on motion information of corresponding pixels in the geometryimage.
 6. The method of claim 1, wherein the prediction informationincludes a flag indicative of applying a selective smoothing insidepatches of the point cloud.
 7. The method of claim 6, wherein theprediction information is indicative of a specific algorithm to selectpoints inside the patches.
 8. The method of claim 7, wherein theprediction information includes parameters for the specific algorithm.9. A method for point cloud compression, comprising: compressing, by aprocessor, a geometry image associated with a point cloud;reconstructing, by the processor, a geometry reconstructed cloudaccording to the compressed geometry image of the point cloud; applying,by the processor, a filter to at least a geometry sample inside a patchof the geometry reconstructed cloud in addition to boundary samples ofthe patch to generate a smoothed geometry reconstructed cloud; andgenerating, by the processor, a texture image for the point cloud basedon the smoothed geometry reconstructed cloud.
 10. The method of claim 9,further comprising: selecting, by the processor, an area inside thepatch with a level of high frequency components higher than a thresholdlevel.
 11. The method of claim 9, further comprising: selecting, by theprocessor, an area inside the patch with a level of motion contenthigher than a threshold level.
 12. The method of claim 10, furthercomprising: detecting, by the processor, edges inside the patch based ondepth values of the geometry reconstructed cloud.
 13. The method ofclaim 11, further comprising: selecting, by the processor, points insidethe patch based on motion information of corresponding pixels in thegeometry image.
 14. The method of claim 9, further comprising:including, in a coded bitstream for the compressed point cloud, a flagindicative of applying a selective smoothing inside patches of the pointcloud.
 15. The method of claim 14, further comprising: including, in thecoded bitstream of the compressed point cloud, an indicator indicativeof a specific algorithm to select points inside the patches to apply theselective smoothing.
 16. An apparatus for point cloud decompression,comprising: processing circuitry configured to: decode predictioninformation of a point cloud from a coded bitstream; reconstruct ageometry reconstructed cloud according to a geometry image of the pointcloud that is decoded from the coded bitstream; apply a filter to atleast a geometry sample inside a patch of the geometry reconstructedcloud in addition to boundary samples of the patch to generate asmoothed geometry reconstructed cloud; and reconstruct points of thepoint cloud based on the smoothed geometry reconstructed cloud.
 17. Theapparatus of claim 16, wherein the processing circuitry is furtherconfigured to: select an area inside the patch with a level of highfrequency components higher than a threshold level.
 18. The apparatus ofclaim 16, wherein the processing circuitry is further configured to:select an area inside the patch with a level of motion content higherthan a threshold level.
 19. The apparatus of claim 17, wherein theprocessing circuitry is configured to: detect edges inside the patchbased on depth values of the geometry reconstructed cloud.
 20. Theapparatus of claim 18, wherein the processing circuitry is configuredto: select points inside the patch based on motion information ofcorresponding pixels in the geometry image.